Joint decomposition of several linked matrices with Principal Component Analysis (PCA)
Usage
jointPCA(
dataset,
group,
comp_num,
weighting = NULL,
max_ite = 100,
max_err = 1e-04,
proj_dataset = NULL,
proj_group = NULL,
enable_normalization = TRUE,
column_sum_normalization = FALSE,
screen_prob = NULL
)Arguments
- dataset
A list of dataset to be analyzed
- group
A list of grouping of the datasets, indicating the relationship between datasets
- comp_num
A vector indicates the dimension of each compoent
- weighting
Weighting of each dataset, initialized to be NULL
- max_ite
The maximum number of iterations for the jointPCA algorithms to run, default value is set to 100
- max_err
The maximum error of loss between two iterations, or the program will terminate and return, default value is set to be 0.001
- proj_dataset
The datasets to be projected on
- proj_group
The grouping of projected data sets
- enable_normalization
An argument to decide whether to use normalizaiton or not, default is TRUE
- column_sum_normalization
An argument to decide whether to use column sum normalization or not, default it FALSE
- screen_prob
A vector of probabilies for genes to be chosen
Value
A list contains the component and the score of each dataset on every component after jointPCA algorithm
Examples
dataset = list(matrix(runif(5000, 1, 2), nrow = 100, ncol = 50),
matrix(runif(5000, 1, 2), nrow = 100, ncol = 50),
matrix(runif(5000, 1, 2), nrow = 100, ncol = 50),
matrix(runif(5000, 1, 2), nrow = 100, ncol = 50))
group = list(c(1,2,3,4), c(1,2), c(3,4), c(1,3), c(2,4), c(1), c(2), c(3), c(4))
comp_num = c(2,2,2,2,2,2,2,2,2)
proj_dataset = list(matrix(runif(5000, 1, 2), nrow = 100, ncol = 50))
proj_group = list(c(TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE))
res_jointPCA = jointPCA(
dataset,
group,
comp_num,
proj_dataset = proj_dataset,
proj_group = proj_group)